
ACMLM [1]} is a fine-tuned BERT [2]}, where an attention layer is introduced to encode the features from both the user and the item.
By predicting masked tokens, this model can produce diverse sentences.

NETE [3]} is a tailored GRU [4]} that incorporates a given feature into the decoding process to generate template-like explanations.
It can also make recommendations.

